Abstract
In this paper we propose the use of Support Vector Machine (SVM) to evaluate system reliability. The main idea is to develop an estimation algorithm by training a SVM on a restricted data set, replacing the system performance model evaluation by a simpler calculation. The proposed approach is illustrated by an example. System reliability is properly emulated by training a SVM with a small amount of information.
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Pereira M.V.F, Pinto L.M.V.G.: “A New Computational Tool for Composite Reliability Evaluation”, IEEE Power System Engineering Society Summer Meeting, 1991, 91SM443-2
Billinton, R. Allan R.N: Reliability Evaluation of Engineering Systems, Concepts and Techniques. Second Edition. Plenum Press. 1992
Reingold E., Nievergelt J., Deo N.: Combinatorial Algorithms: Theory and Practice, Prentice Hall, New Jersey, 1977
Papadimitriou C. H., Steiglitz K.: Combinatorial Optimization: Algorithms and Complexity, Prentice Hall, New Jersey, 1982
Billinton, R. Li W.: Reliability Assessment of Electric Power System Using Monte Carlo Methods. Plenum Press. 1994
Dubi A.: “Modeling of Realistic System with the Monte Carlo Method: A Unified System Engineering Approach”, Proc. Annual Reliability and Maintainability Symposium, Tutorial Notes, 2001
Pohl E.A., Mykyta E.F.: “Simulation Modeling for Reliability Analysis”, Proc. Annual Reliability and Maintainability Symposium, Tutorial Notes, 2000
Marseguerra M., Masine R., Zio E., Cojazzi G.: “Using Neural Networks as Nonlinear Model Interpolators in Variance Decomposition-Based Sensitivity Analysis”, Third International Symposium on Sensitivity Analysis of Model Output, Madrid, June 2001.
Merrill H., Schweppe F.C., “Energy Strategic Planning for Electric Utilities Part I and Part II, Smarte Methodology”, IEEE Transactions on Power Apparatus and Systems, Vol. PAS-101, No. 2, February 1982
Sapio B.: “SEARCH (Scenario Evaluation and Analysis through Repeated Cross-impact Handling): A new method for scenario analysis with an application to the Videotel service in Italy”, International Journal of Forecasting, (11) 113–131, 1995
Mukerji R., Lovell B.: “Creating Data Bases for Power Systems Planning Using High Order Linear Interpolation”, IEEE Transactions on Power Systems, Vol. 3, No. 4, November 1988
Rahman S., Shrestha G.: “Analysis of Inconsistent Data in Power Planning”, IEEE Transactions on Power Systems, Vol. 6, No. 1, February 1991
Burges C.: “tutorial on Support Vector Machines for Patter Recognition”, http://www.kernel-machines.org/
Platt J.:”Fast Training of Support Vector Machines using Sequential Minimal Optimization”, http://www.research.microsoft.com/~jplatt
Fishman G.: “A Comparison of Four Monte Carlo Methods for Estimating the Probability of s-t Connectedness”, IEEE Transaction on Reliability, Vol. R-35, No. 2, June 1986
Cristianini N., Shawe-Taylor J.: “An introduction to Support Vector Machines”, Cambridge University Press, 2000
Campbell C.: “Kernel Methods: A survey of Current Techniques”, http://www.kernel-machines.org/
Campbell C.: “An Introduction to Kernel Methods”, In R.J. Howlett and L.C. Jain, editors, Radial Basis Function Networks: Design and Applications, page 31. Springer Verlag, Berlin, 2000
Veropoulos K., Campbell C, Cristianini N.: “Controlling the Sensitivity of Support Vector Machines”, Proceedings of the International Joint Conference on Artificial Intelligence, Stockholm, Sweden, 1999 (IJCAI99), Workshop ML3, p. 55–60.
Yoo Y.B., Deo N.: “A Comparison of Algorithm for Terminal-Pair Reliability”, IEEE Transaction on Reliability, Vol. 37, No. 2, June 1988
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Sanseverino, C.M.R., Moreno, J.A. (2002). Reliability Evaluation Using Monte Carlo Simulation and Support Vector Machine. In: Sloot, P.M.A., Hoekstra, A.G., Tan, C.J.K., Dongarra, J.J. (eds) Computational Science — ICCS 2002. ICCS 2002. Lecture Notes in Computer Science, vol 2329. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-46043-8_14
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DOI: https://doi.org/10.1007/3-540-46043-8_14
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